Deep learning for audio classification is a broad research area with applications like Keyword Spotting (KWS), urban sound identification, etc. KWS is an important application for detecting keywords of importance to specific users, which could be used as voice commands to on-device personal assistants such as Amazon's Alexa, Apple's Siri, etc. [1]}. Urban environment sound classification is another interesting application particularly in context-aware computing, urban informatics [2]}. The emergence of deep neural networks have conveniently alleviated problems of creating shallow (hand-picked) features to achieve state-of-the-art performance in such acoustic classification tasks [3]}, [4]}. With the recent compute capabilities vested in resource-constrained devices, there is a huge research focus on audio classification using on-device deep learning [5]}, [6]}.
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